Tag: operational efficiency

  • How Naol Bassaye’s Seeft Brings Enterprise-Grade Process Intelligence to Mid-Sized Businesses

    How Naol Bassaye’s Seeft Brings Enterprise-Grade Process Intelligence to Mid-Sized Businesses

    For years, advanced process-intelligence tools have been reserved for the world’s largest companies—organizations that can afford complex deployments, external consultants, and dedicated teams to manage lengthy implementation cycles. Meanwhile, many mid-market businesses face similar operational challenges but lack the resources to adopt the same technology.

    Naol Mekuriya Bassaye saw this divide firsthand during his two years at Celonis, where he developed process-intelligence solutions for multinational enterprises across energy, aviation, automotive, pharmaceuticals, medical devices, and industrial manufacturing. His work gave him an inside look at how large companies use operational data to trace activity, locate inefficiencies, and improve cross-department workflows—and it also revealed how many businesses remained locked out of that system.

    “Complexity does not begin at the Fortune 500 level, but access to the best tools often does,” said Bassaye. “A mid-sized manufacturer can face demanding operational problems without the budget, implementation support, or internal resources available to a global enterprise.”

    Now, as co-founder and chief technology officer of Seeft, Bassaye is adapting enterprise-grade process intelligence for mid-sized businesses that make, move, and sell physical goods. His approach goes beyond simply lowering the price of a large-company platform; he believes mid-market businesses need a fundamentally different product philosophy.

    “A smaller company does not need a diluted enterprise system,” he explained. “It needs software built around its own operating reality. The setup has to be faster, the value has to arrive sooner, and the product cannot assume there is a large transformation team waiting to manage it.”

    Seeft is entering the market through the procure-to-pay workflow—a process that crosses several departments and offers a contained opportunity to prove the value of a broader operational system. Instead of requiring a drawn-out technology program, the goal is to help a company identify meaningful opportunities within one to two weeks.

    Bassaye sees speed as a critical component of accessibility. A lower subscription price offers little benefit if a business must still spend months preparing data, coordinating consultants, and assigning employees to support implementation.

    “Time is one of the costs people forget to measure,” he said. “A product can look affordable on paper and still be unrealistic once you account for the people and effort required to make it useful.”

    His perspective has been shaped by experiences beyond Celonis. As former CTO of an aerospace workflow-optimization company, Bassaye learned firsthand how weak implementation can have serious consequences. In one case, a problem remained unresolved after nearly two months of remote discussions. He visited the site, spent three days observing work directly, and compressed the investigation into that shorter period.

    “The issue became clearer once I could see the environment and watch the handoffs,” he recalled. “People often describe a process based on how it is supposed to function. The product has to account for how the work is actually completed.”

    That lesson now shapes his approach to mid-market customers. Software built for operational teams must reflect workarounds, informal decisions, and practical constraints that rarely appear in a formal process map.

    Bassaye also brings experience building products outside traditional enterprise engagements. He created a synthetic data generator used by more than 10,000 people, producing over 2 billion data points. The project pushed him to consider adoption, usability, and scale beyond technical architecture.

    “Building something technically sound is only one part of the job,” he said. “A product has to make sense to the person using it. If the value requires constant explanation, the design is probably asking too much from the customer.”

    His work has attracted attention from selective startup programs. Three Sigma, his previous company, was accepted into Antler’s UK Spring cohort (with an acceptance rate below 2%) and later included in a £1.7 million investment round covering 14 businesses chosen from over 5,000 applicants. Bassaye has since received backing for Seeft through Entrepreneur First.

    “A customer does not care that a product was selected by a respected program,” he noted. “They care whether their team can adopt it without reorganizing the entire company.”

    That standard has become increasingly relevant as artificial intelligence lowers the cost of building advanced software. Capabilities that once required extensive custom development can now be delivered more efficiently, but Bassaye cautions that easier development does not automatically produce a useful operational tool. Mid-market companies still need technology that respects their staffing limits, protects critical workflows, and reaches meaningful results without forcing an enterprise-scale implementation model.

    “The opportunity is larger than making old software cheaper,” Bassaye said. “AI gives us the chance to redesign the category around companies that were never well served by it in the first place.”

    Seeft’s initial focus is procurement, but Bassaye plans to extend the same approach into collections, inventory, and fulfillment—each area affecting working capital and requiring coordination across multiple functions. The larger ambition is to reduce the operational advantage that has traditionally come with size. A global corporation may always have more resources, but Bassaye believes sophisticated visibility should no longer depend entirely on budget or headcount.

    Leaders in these companies—often a COO or CFO of a firm large enough to have real process complexity but small enough that they still carry most of it themselves—rarely lack judgment about their own business. What they lack is the staff to assemble the picture that judgment depends on: the week of analyst time that turns scattered ERP records into a ranked list of what is actually costing money. Software that does that assembly doesn’t replace the decision; it moves the operator from reconstructing what happened to deciding what to do about it, and it shortens the distance between noticing a problem and acting on it.

    “Good operational judgment begins with having a clear view of the work,” Bassaye concluded. “Mid-market leaders should not have to operate with less clarity simply because enterprise technology was not built for them.”

  • The COO’s Playbook for Autonomous Operations With AI Agents

    The COO’s Playbook for Autonomous Operations With AI Agents

    Chief Operating Officers have long been responsible for keeping enterprise operations efficient. Today, that mandate is shifting. As AI agents—software systems that can process information, make decisions, and execute actions—become more capable, COOs are moving beyond managing workflows and starting to re-engineer them from the ground up.

    Traditional automation tools followed fixed instructions. AI agents, by contrast, analyze data, decide what to do next, and complete tasks across multiple enterprise systems with minimal human involvement. The result is a transition from using AI to assist employees to using AI as an active participant in daily operations.

    From Automation to Autonomous Workflows

    Consider a supply chain process. An AI agent can monitor inventory levels, forecast demand, detect shortages, place orders, and notify managers only when approval is required. Instead of automating isolated steps, AI agents connect different business processes into a single, continuous stream.

    Where Companies Are Deploying AI Agents

    Organizations are already applying AI agents in areas where manual work slows decision-making:

    • Supply chain: AI agents track stock levels, predict demand, flag supplier risks, and recommend purchases, helping teams respond to disruptions faster and avoid inventory shortages.
    • Finance: Finance teams use AI agents for invoice processing, account reconciliation, anomaly detection, and report generation, reducing manual steps while maintaining control.
    • Customer service: AI agents handle routine inquiries, categorize requests, and route complex cases to human agents, freeing teams to focus on higher-value interactions.
    • IT operations: Technology teams use AI agents to monitor networks, identify potential failures, diagnose root causes, and in some cases take preventive action before outages affect customers.

    The COO’s Priorities Are Evolving

    As AI becomes embedded in daily operations, the COO role is expanding beyond efficiency. The new priority is building an operating model where humans and AI work together effectively. That requires investments in high-quality data, governance, cybersecurity, and employee training—not just AI deployment.

    COOs are increasingly asking not which tasks can be automated, but which business processes can run autonomously with sufficient human control. This is as much an organizational change as a technical one. Applying AI to existing processes in isolation often delivers limited value.

    Building Autonomous Operations

    Most organizations are taking a phased approach rather than trying to automate everything at once. The first step is identifying repetitive, high-volume workflows with clear business value. Next, companies improve data quality, develop governance policies, and define where human intervention is required.

    Interoperability is another critical issue. AI agents need to work within enterprise applications, cloud platforms, and corporate databases—not in standalone systems. Companies that treat AI as an enterprise-wide strategy are more likely to succeed than those that deploy it in departmental silos.

    Why This Matters

    Enterprise operations are undergoing a fundamental shift. The question for COOs is no longer whether to use AI, but how. Organizations that combine autonomous processes with strong governance, reliable data, and human oversight will be better positioned to improve efficiency and adapt quickly to changing market demands.

    The Challenges Ahead

    The move toward autonomous operations also brings risks. Many companies still struggle with poor data quality, outdated IT infrastructure, and siloed enterprise architecture, all of which can undermine even the most sophisticated AI agents.

    Governance is a growing concern, especially when AI systems make financial or customer-related decisions. Companies need transparent methods for explaining AI-driven decisions. Without them, AI can add new problems instead of solving existing ones.

    Key Questions COOs Are Asking About AI Agents

    • What are AI agents? AI agents are intelligent software systems that analyze data, make decisions, and execute multi-step tasks with minimal human intervention.
    • How do AI agents help COOs? They automate routine workflows, improve operational efficiency, support faster decision-making, and free COOs to focus on strategic priorities.
    • Which departments benefit most? Supply chain, finance, customer service, procurement, and IT operations are among the earliest and largest adopters.
    • Can AI agents replace employees? No. They handle repetitive tasks while humans oversee critical decisions, governance, compliance, and complex problem-solving.
    • What is the biggest adoption challenge? Ensuring high-quality data, strong governance, cybersecurity, and seamless integration with existing enterprise systems.
  • 10 CEO Playbooks to Drive Innovation Without Increasing Budgets

    10 CEO Playbooks to Drive Innovation Without Increasing Budgets

    Overview

    Build innovation through smarter leadership, agile teams, customer insights, and disciplined decision-making every day. Reduce costs by eliminating bureaucracy, improving operations, and maximizing existing business resources effectively together. Create sustainable growth using data-driven strategies, experimentation, and continuous learning across every business function consistently.

    Innovation has long been associated with bigger budgets, larger teams, and heavy investments in technology. This mentality is beginning to shift. In an environment where companies must operate on narrower profit margins, CEOs must deliver growth amid constrained costs.

    It is not necessarily the companies spending the most money that win out. Instead, it is those who make quick decisions, empower their people, maximize their assets, and meet customer demands promptly. Innovation is not about writing bigger checks but about fostering a culture of smart ideas.

    10 CEO Playbooks to Drive Innovation Without Increasing Costs

    Here are 10 CEO-level playbooks that aid innovation without raising costs:

    1. Make Lemonade Out of Lemons

    Constraints often spark innovation. Rather than providing more money to a team, ask them to come up with innovative solutions by leveraging the resources they already have. Working under constraints fosters innovation and cuts waste.

    2. Small Before Large

    New and innovative ideas may not be fully implemented through their entire process cycle within the business organization. Implementing pilot projects, prototypes, or MVPs will enable the business organization to gather important information about innovations without incurring costly mistakes.

    3. Get Rid of Bottlenecks

    The process of innovation is impeded by lengthy decision-making cycles. The decrease of stages of approval, the holding of fewer unnecessary meetings, and the delegation of decision-making power to teams will speed up the innovation process. Sometimes, getting rid of bureaucracy works better than additional financing.

    4. Assign Major Tasks to Small Teams

    It is unlikely that big teams will come up with innovations. Cross-functional small teams with clearly defined responsibilities can significantly increase efficiency in implementing innovation processes. People feel more responsible if they have decision-making power.

    5. Make the Business Work Better

    Innovation is not just about inventing new products. Simplifying and automating business processes, improving customer service, and making operations more efficient help increase productivity and reduce costs. Sometimes, competitiveness lies in doing things more efficiently.

    6. Innovate According to Customer Demand

    Customers can first see any shortcomings in your products and services. Feedback can be obtained through customer reviews, surveys, support groups, and personal interactions to identify issues that need improvement. These usually turn out to be very practical innovations.

    7. Always Have Data Behind Your Decisions

    Evidence always makes good ideas better. Analyze customer behavior, operational performance, and product usage to identify opportunities for innovation. While doing this, do not waste money on projects that have no tangible success.

    8. Explore Opportunities in Your Current Assets

    Sometimes innovation has nothing to do with coming up with something new. Your existing technology, talented employees, manufacturing processes, intellectual property, and even your customers can all be used to develop ways to earn additional revenue.

    9. Build a Culture That Learns from Failure

    The fear of failure means experiments will never happen. Employees need to be inspired to conduct experiments, learn from their failures, and share their insights with others. Even though not all experiments will succeed, they all provide important information that helps make future decisions.

    10. Align Innovations with Corporate Goals

    Innovations should address a company’s problem. If the goal of an innovation is to improve customer experience, reduce expenses, increase productivity, generate additional income, etc., any innovation should deliver results for a company. Otherwise, an innovation is not worth the money spent on it.

    Why This Matters

    At this point, there isn’t really a clear correlation between innovation and larger budgets. Companies that try to innovate by making sharper decisions, staying nimble, pulling real customer insights, and improving efficiency can end up moving faster, becoming more profitable, adapting to market shifts, and even outdoing competitors without increasing expenses.

    Conclusion

    Being an awesome company doesn’t have to mean being an awesome spender. Great CEOs tend to make their organizations more efficient by cutting costs, giving real trust to team members, leaning into learning, and making better decisions. In the business world, where every rupee counts, innovation that does not inflate costs can become the signature of strong leadership.

    Frequently Asked Questions

    1. Can businesses innovate without increasing budgets?

    Yes. Smarter leadership, better resource allocation, agile teams, and efficient processes can drive innovation without significantly increasing operational spending.

    2. Why are small teams more effective for innovation?

    Small, cross-functional teams make faster decisions, collaborate efficiently, adapt quickly, and take greater ownership of delivering measurable business outcomes.

    3. How does customer feedback support innovation?

    Customer insights reveal pain points, helping businesses improve products, services, and experiences while reducing unnecessary development costs and risks.

    4. What role does data play in innovation?

    Data helps leaders identify opportunities, measure performance, eliminate ineffective initiatives, and make informed decisions that improve business results.

    5. Why should CEOs focus on operational innovation?

    Improving internal processes increases productivity, reduces costs, enhances customer experience, and creates long-term competitive advantages without major investments.

  • How AI-Powered Voice Workflows Are Reshaping Enterprise Communication

    How AI-Powered Voice Workflows Are Reshaping Enterprise Communication

    AI-powered voice workflows are transforming enterprise communication by improving consistency, scalability and operational efficiency. While empathy remains a human strength, businesses adopting AI with strong governance are expected to gain a long-term competitive advantage.

    Authored by Subhash Kalluri, Founder, FreJun

    Having spent close to a decade building telephony products, the questions enterprises asked were predictable for most of that time. Uptime. Integrations. Per-minute pricing. Somewhere in the last year, the first question changed. Now the person across the table wants to know whether a voice agent can handle their customers without embarrassing the brand. That single shift is enough to tell where this industry is going.

    So let’s be straight about both sides of this. Where voice agents are genuinely pulling ahead and where they still fall short. The second part matters more than most vendors are willing to say out loud.

    Why the shift is happening

    The first reason is accuracy. A trained voice agent gives the same correct answer at 2 in the afternoon and at 2 in the morning. It does not have a bad day, it does not forget the new refund policy that was rolled out last week, and it does not give a customer in Pune one version of the truth and a customer in Coimbatore another. For any enterprise that has struggled to keep a distributed support team consistent, this alone is a big deal.

    Then there is the question of improvement. When a human agent has a difficult call and finds a clever way to resolve it, that learning usually stays with that one person. Maybe it gets shared in a team huddle, maybe it does not. With a voice workflow, studying what worked and feeding it back into the system means every conversation after that benefits. The service quietly gets better month on month instead of resetting every time someone resigns.

    The factor that gets the most attention in the boardroom, though, is cost, and rightly so. Demand in most Indian businesses is not flat. It spikes during a festive sale, during a product launch, and during the days after a billing cycle. With human teams, enterprises are forced to either be overstaffed for the peak and carry that cost through the lean months or understaff and let customers wait. Voice workflows let capacity scale up and down with actual demand. The cost reflects what is used, not what might be needed.

    There is also a less glamorous benefit that operations leaders will immediately recognise. Human error in handoffs. So much breaks in the gap between Tier 1 and Tier 2, between the day shift and the night shift, and between the person who took the complaint and the person expected to close it. Information gets dropped, context gets lost, and the customer repeats themselves three times. A well-designed voice workflow carries the full context through every step, so far less falls through the cracks.

    And finally, ramp-up time. Anyone who has hired customer-facing executives in this country knows the real cost is not just the salary. It is the weeks of training, shadowing, and quality checks before that person is actually productive. A voice agent is ready on day one. When demand jumps suddenly, there is no waiting a month to be effective.

    Where it still falls short

    Now the honest part is that the industry is not there yet on the things that actually make customers loyal.

    The human-like experience is still missing. One can feel, within a few seconds, when they are not talking to a person, and that gap has not closed. Empathy is the bigger one. When a customer is angry or anxious or genuinely upset, an agent today cannot read that emotion and respond to it the way a good human executive can. And there is a kind of magic that happens when a frontline employee decides to go above and beyond, breaks a small rule to make things right, and takes ownership of a problem that was never theirs to own. That moment, the one a customer remembers and tells their friends about, cannot yet be manufactured by software.

    An honest acknowledgement of existing gaps is a strength rather than a weakness. By addressing these realities transparently, organisations can build deeper trust with customers and create stronger alignment within their teams.

    Where the industry is headed

    The honest read is that voice agents will take over the majority of conversations in the enterprise. Not all of them, and not overnight. Humans will stay very much in the loop. What seems likely is a gradual handover, where step by step the number of routine conversations handled by people comes down and voice workflows pick up more of the volume, freeing humans to handle the complex, emotional, high-value moments that actually need them.

    But this only works with proper checks and balances. Wherever these workflows touch enterprise data and decision-making, there has to be a clear, well-governed way to handle that data and those steps. The goal is that the technology improves overall performance without ever quietly damaging the outcomes the organisation cares about. Get that governance right, and voice workflows do not just cut cost. They make the whole operation better.

    That is the future this industry is building towards, and the enterprises that move thoughtfully, rather than either rushing in or sitting it out, are the ones who will come out ahead.